Object detection method and device

Abstract

Disclosed is an object detection method used to detect an object in an image pair corresponding to a current frame. The image pair includes an original image of the current frame and a disparity map of the same current frame. The original image of the current frame includes at least one of a grayscale image and a color image of the current frame. The object detection method comprises steps of obtaining a first detection object detected in the disparity map of the current frame; acquiring an original detection object detected in the original image of the current frame; correcting, based on the original detection object, the first detection object so as to obtain a second detection object; and outputting the second detection object.

What is claimed is: 
     
       1. An object detection method used to detect an object in an image pair corresponding to a current frame, the image pair including an original image of the current frame and a disparity map of the same current frame, the original image of the current frame including at least one of a grayscale image and a color image of the current frame, the object detection method comprising:
 obtaining a first detection object detected in the disparity map of the current frame; 
 acquiring an original detection object detected in the original image of the current frame; 
 correcting first, based on the original detection object, the first detection object so as to obtain a second detection object; 
 outputting the second detection object 
 obtaining a historical object template and plural historical weight coefficients for the historical object template, the historical object template being divided into plural historical segments which are respectively assigned the plural historical weight coefficients; 
 determining, based on the historical object template, one or more original candidate objects in the original image of the current frame; 
 respectively calculating, based on the plural historical segments and the plural historical weight coefficients, weighted similarities between the historical object template and the one or more original candidate objects; and 
 determining an original candidate object, whose relevant weighted similarity is maximum, as the original detection object. 
 
     
     
       2. The object detection method according  claim 1 , wherein, the respectively calculating, based on the plural historical segments and the plural historical weight coefficients, the weighted similarities between the historical object template and the one or more original candidate objects includes:
 regarding each of the one or more original candidate objects, 
 dividing the corresponding original candidate object into plural original segments in a same way as the historical object template is divided into the plural historical segments; 
 respectively calculating plural segment similarities between the plural original segments and the plural historical segments; 
 respectively weighting the plural segment similarities by using the plural historical weight coefficients; and 
 calculating a sum of the plural weighted segment similarities so as to serve as the weighted similarity between the corresponding original candidate object and the historical object template. 
 
     
     
       3. The object detection method according to  claim 1 , further comprising:
 correcting second, based on the first detection object, the plural historical weight coefficients so as to obtain plural current weight coefficients which will serve as historical weight coefficients with respect to an original image of a next frame. 
 
     
     
       4. The object detection method according to  claim 3 , wherein, the correcting second includes:
 respectively determining plural disparity point distributions of the first detection object in plural original segments obtained by dividing the original detection object in a same way as the historical object template is divided into the plural historical segments; and 
 respectively generating, based on the plural disparity point distributions, the plural current weight coefficients for the plural original segments of the original detection object. 
 
     
     
       5. An object detection method used to detect an object in an image pair corresponding to a current frame, the image pair including an original image of the current frame and a disparity map of the same current frame, the original image of the current frame including at least one of a grayscale image and a color image of the current frame, the object detection method comprising:
 obtaining a first detection object detected in the disparity map of the current frame; 
 acquiring an original detection object detected in the original image of the current frame; 
 correcting, based on the original detection object, the first detection object so as to obtain a second detection object; 
 outputting the second detection object; 
 obtaining a historical detection object in a disparity map of a historical frame; 
 based on the historical detection object, estimating, in the disparity map of the current frame, a current estimation object corresponding to the historical detection object; 
 determining one or more first candidate objects in the disparity map of the current frame; 
 respectively determining matching degrees between the current estimation object and the one or more first candidate objects; and 
 determining a first candidate object, whose relevant matching degree is maximum, as the first detection object. 
 
     
     
       6. The object detection method according to  claim 5 , wherein, the acquiring includes:
 acquiring the original detection object detected in a predetermined range of the current estimation object in the original image of the current frame. 
 
     
     
       7. The object detection method according to  claim 1 , wherein, the correcting first includes:
 determining, based on the original detection object, a current correction region in the disparity map of the current frame; and 
 generating the second detection object based on at least a set of disparity points included in the current correction region. 
 
     
     
       8. The object detection method according to  claim 1 , before the correcting first, further comprising:
 a determination step of determining whether the first detection object and the original detection object match each other. 
 
     
     
       9. The object detection method according to  claim 8 , wherein, the determining whether the first detection object and the original detection object match each other includes at least one of steps of:
 determining whether an overlap area between the first detection object and the original detection object is greater than or equal to a predetermined threshold, and if so, then determining that the first detection object and the original detection object match each other; and 
 determining whether a distance between the first detection object and the original detection object is less than or equal to a predetermined threshold, and if so, then determining that the first detection object and the original detection object match each other. 
 
     
     
       10. An object detection device used to detect an object in an image pair corresponding to a current frame, the image pair including an original image of the current frame and a disparity map of the same current frame, the original image of the current frame including at least one of a grayscale image and a color image of the current frame, the object detection device comprising:
 a memory including computer readable instructions; and 
 one or more processors configured to execute the computer readable instructions to perform
 obtaining a first detection object detected in the disparity map of the current frame; 
 acquiring an original detection object detected in the original image of the current frame; 
 correcting first, based on the original detection object, the first detection object so as to obtain a second detection object; 
 outputting the second detection object; 
 obtaining a historical object template and plural historical weight coefficients for the historical object template, the historical object template being divided into plural historical segments which are respectively assigned the plural historical weight coefficients; 
 determining, based on the historical object template, one or more original candidate objects in the original image of the current frame; 
 respectively calculating, based on the plural historical segments and the plural historical weight coefficients, weighted similarities between the historical object template and the one or more original candidate objects; and 
 determining an original candidate object, whose relevant weighted similarity is maximum, as the original detection object. 
 
 
     
     
       11. The object detection device according to  claim 10 , wherein the one or more processors are further configured to perform
 dividing the corresponding original candidate object into plural original segments in a same way as the historical object template is divided into the plural historical segments; 
 respectively calculating plural segment similarities between the plural original segments and the plural historical segments; 
 respectively weighting the plural segment similarities by using the plural historical weight coefficients; and 
 calculating a sum of the plural weighted segment similarities so as to serve as the weighted similarity between the corresponding original candidate object and the historical object template. 
 
     
     
       12. The object detection device according to  claim 10 , wherein the one or more processors are further configured to perform
 correcting second, based on the first detection object, the plural historical weight coefficients so as to obtain plural current weight coefficients which will serve as historical weight coefficients with respect to an original image of a next frame. 
 
     
     
       13. The objet detection device according to  claim 12 , wherein the correcting second includes:
 respectively determining plural disparity point distributions of the first detection object in plural original segments obtained by dividing the original detection object in a same way as the historical object template is divided into the plural historical segments; and 
 respectively generating, based on the plural disparity point distributions, the plural current weight coefficients for the plural original segments of the original detection object.